With the development of the Internet and related technologies and due to the continuous increase of the bandwidth capabilities of the communication networks, the transmission of video information over the Internet becomes more popular and more reliable. In the past, the quality of the transmitted video signals over the Internet has been low for the following reasons. In conversational video telephony services, the video encoder usually applies a predetermined rate control function, which tries to maintain an average source bit-rate constant as close as possible to a given target bit-rate.
In packet switched networks, like Video Telephony over IP (VToIP), a transport delay of video packets will vary due to several reasons. One of the reasons is the varying load on the transport links, which may cause a low frequency fluctuation of transport delay and even bursty congestion peaks, as shown for example in FIG. 1. FIG. 1 shows an example of a congestion peak between 40 and 50 s. The video transport delay is occurring as shown in FIG. 1, when the transmitter/receiver has no target rate adaptation and the buffer size is infinite. The infinite buffer size means that packets are never lost due to buffer overflow. The congestion peak may happen because of a sudden increase in Internet traffic and may last up to several seconds. In FIG. 1 the congestion lasts from 10 s to 50 s. The congestion peaks conventionally may cause a slow down of the transmission of video data, which results at a receiver side, in a degradation of a quality of a perceived image, like for example, a delay variation, packet losses, or a freezing and jumping of a displayed image. Thus, in the past it was unlikely, using the Internet and not a dedicated transmission network, to receive a good quality video signal that lasts over a certain time, for example, the couple of minutes which might be necessary for having a videoconference or a video phone conversation.
The integrity of the decoded video is usually damaged due to losses, as it is known that video compression is based on motion compensation, which uses previous decoded frames as references for the future frames. How long these disturbances last will depend on the Intra-refresh (IR) process (i.e., encoding a frame or parts of the frame without motion compensation) which is used. The IR process is needed because a transmission of video data over a wireless network can be unreliable due to channel loss. Errors resulting from channel loss can adversely impact the quality of video presented to a user. In particular, channel errors can reduce not only the quality of a current frame, but also subsequent Inter-coded frames generated from the current frame using motion estimation and compensation techniques. To limit propagation of channel-induced errors from one frame to another, a video encoder typically applies an IR process. The IR processes are described, for example, by the Moving Picture Experts Group (MPEG) or the International Telecommunication Union (ITU).
To address these problems, there were proposed several state of the art techniques. Some of the existing techniques rely on admission control methods, which typically block new sessions when a loaded communication network (or a loaded link between nodes) is detected. Some of the techniques use video target rate adaptation, which is based on a feedback from a video receiver to a sender.
However, one problem associated with the existing video target rate adaptation methods is that they are usually based on some average aggregate maximum capacity, which may be insensitive to temporary (short time) congestions caused by statistical multiplexing of variable rate streams. The temporary congestions are relevant and should be addressed if best effort traffic is competing for the same resources. There are also more advanced measurement based dynamic admission control methods, but these methods are usually based on link-by-link level rather than end-to-end level. The link-by-link level refers, for example, to a system of multiple base stations and mobile phones. Two mobile phones are connected to each other via a plurality of links, each link connecting either a base station to the mobile phone or the base station to another base station or to another network node (like a radio network controller, gateway or router). The number of links between the two mobile phones depends on the geographic distance between the phones and whether the mobile phones are located in the same radio access network or not. Thus, in this particular example, more than two links may exist between the two mobile phones and the control methods measure various parameters for each link. Unlike the link-by-link level, the end-to-end level monitors the signals received at the two mobile phones and not at the links between various network nodes.
Feedback based target rate adaptation methods generally use end-to-end level monitoring. However, the existing methods are based on a relaxed feedback mechanism, which usually cannot cope with the bursty congestion peaks. Another problem of the existing methods is the poor recovery of the target rate back to its original level, after the congestion period is over. For example, a traditional method adapts a target rate based on packet loss measurements. A first problem of this method is that it takes a long time to receive reliable measurements of the packet loss rate (PLR) at the transmitter from the receiver. When the PLR is considered to be high, the target rate on the encoder is reduced, but this process happens too late, and the congestion peak may even be over already. Still another problem associated with this method is that when the PLR=0, it is difficult to know how much to increase the target rate without causing packet losses and deteriorating the quality of the transmitted video, because the combination of (i) a measured PLR that is 0 and (ii) a reduced target rate does not necessarily imply that the congestion is over. To avoid this problem, the target rate in the existing methods recovers very slowly or it may even stay down permanently, which results in the video encoder using a low target rate and consequently, unnecessarily degrades the perceived quality of the transmitted video even after a temporary congestion peak.
Thus, it is an objective of the following exemplary embodiments to address and solve one or more of the above discussed problems of the existing methods and techniques.